Nottingham Trent University

What you'll study

The programme blends theoretical foundations with hands-on application. You will study core topics such as statistical inference, supervised and unsupervised machine learning, and data engineering, alongside modules on data visualisation, ethical and legal issues in data science, and scalable computing.

• Statistical Methods for Data Science – probability, estimation, hypothesis testing and regression techniques for real-world datasets.
• Machine Learning and Predictive Modelling – supervised and unsupervised algorithms, model evaluation, feature engineering and model selection.
• Programming and Software for Data Science – practical skills in languages and tools commonly used in industry (such as Python, R and relevant libraries), version control and reproducible workflows.
• Data Engineering and Big Data Technologies – database design, SQL, data pipelines, cloud fundamentals and working with large-scale data processing frameworks.
• Data Visualisation and Communication – techniques to present analysis results clearly for different audiences using visualisation libraries and dashboarding tools.
• Ethics, Governance and Professional Practice – privacy, data protection, fairness and the societal impact of data-driven systems.
• Optional modules – depending on cohort and availability, options may include natural language processing, time series analysis, deep learning or domain-specific applications (e.g. healthcare analytics, business analytics).
• MSc Project or Dissertation – an independent, substantial project that applies methods learned to a practical or research problem, often undertaken with external partners or using real datasets.

Entry requirements

Applicants are normally expected to hold an undergraduate degree in a numerate discipline such as computer science, mathematics, statistics, engineering, physics, economics or a related subject. A first-class or upper second-class honours degree (or an international equivalent) is normally preferred. Applicants with a lower-class degree but with relevant professional experience or demonstrable programming and quantitative skills may also be considered.

You should be comfortable with programming and basic statistics; introductory experience with Python or R and with linear algebra and probability will help you to succeed. International applicants whose first language is not English will need to meet the university's English language requirements.

Career prospects

Graduates go on to roles across industry, public sector and research. Typical job titles include data scientist, machine learning engineer, data analyst, data engineer, business intelligence developer and analytics consultant. The programme prepares you for work in sectors such as finance, healthcare, retail, technology, government and consultancy, as well as for further study at PhD level.

The combination of practical projects, industry-relevant tools and the dissertation provides material for professional portfolios and employer discussions, and the university's careers service supports CVs, interview preparation and employer links.

Why study at Nottingham Trent University

Nottingham Trent University has a strong emphasis on applied learning and industry engagement, giving MSc Data Science students access to practical project work, modern computing facilities and opportunities to collaborate with businesses and research centres. Teaching is delivered by staff with both academic and industrial experience, and the curriculum is designed to reflect current tools and practices used by employers.

The university's campus environment and dedicated support services for postgraduate students help with academic development, professional skills and transition into employment. The course's focus on ethical practice, scalable computing and communication skills aims to produce graduates who can both build models and translate insights into organisational value.